Faculty Opinions recommendation of Gene expression elucidates functional impact of polygenic risk for schizophrenia.

ABSTRACTOur group developed a transcriptome-based polygenic risk score (T-PRS) that uses common genetic variants to capture ‘depression-like’ shifts in cortical gene expression. Here, we mapped T-PRS onto diagnosis and symptom severity in major depressive disorder (MDD) cases and controls from the Psychiatric Genomics Consortium (PGC). To evaluate potential mechanisms, we further mapped T-PRS onto discrete measures of brain morphology and broad depression risk in healthy young adults. Genetic, self-report, and/or neuroimaging data were available in 29,340 PGC participants (59% women; 12,923 MDD cases, 16,417 controls) and 482 participants in the Duke Neurogenetics Study (DNS: 53% women; aged 19.8±1.2 years). T-PRS was computed from SNP data using PrediXcan to impute cortical expression levels of MDD-related genes from a previous post-mortem transcriptome meta-analysis. Sex-specific regressions were used to test effects of T-PRS on depression diagnosis, symptom severity, and Freesurfer-derived subcortical volume, cortical thickness, surface area, and local gyrification index in the PGC and DNS samples, respectively. T-PRS did not predict depression diagnosis (OR=1.007, 95%CI=[0.997-1.018]); however, it correlated with symptom severity in men (rho=0.175, p=7.957×10−4) in one large PGC cohort (N=762, 48% men). In DNS, T-PRS was associated with smaller amygdala volume in women (β=-0.186, t=-3.478, p=.001) and less prefrontal gyrification (max≤-2.970, p≤.006) in both sexes. In men, prefrontal gyrification mediated an indirect effect of T-PRS on broad depression risk (b=.005, p=.029), indexed using self-reported family history of depression. Depression-like shifts in cortical gene expression predict symptom severity in men and may contribute to disease vulnerability through their effect on cortical gyrification.

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eVIP2: Expression-based variant impact phenotyping to predict the function of gene variants

10.1101/872028 ◽

2019 ◽

Author(s):

Alexis M. Thornton ◽

Lishan Fang ◽

Casey O’Brien ◽

Alice H. Berger ◽

Marios Giannakis ◽

...

Keyword(s):

Gene Expression ◽

Negative Regulator ◽

Gene Variants ◽

Loss Of Function ◽

Gain Of Function ◽

Functional Impact ◽

Function Change ◽

Function Loss ◽

Pathways Analysis ◽

Reporter Assays

AbstractWhile advancements in genome sequencing have identified millions of somatic mutations in cancer, their functional impact is poorly understood. We previously developed the expression-based variant impact phenotyping (eVIP) method to use gene expression data to characterize the function of gene variants. The eVIP method uses a decision tree-based algorithm to predict the functional impact of somatic variants by comparing gene expression signatures induced by introduction of wild-type versus mutant cDNAs in cell lines. The method distinguishes between variants that are gain-of-function, loss-of-function, change-of-function, or neutral. We present eVIP2, software that allows for pathway analysis (eVIP Pathways) and usage with RNA-seq data. To demonstrate the eVIP2 software and approach, we characterized two recurrent frameshift variants in RNF43, a negative regulator of Wnt signaling, frequently mutated in colorectal, gastric and endometrial cancer. RNF43 WT, RNF43 R117fs, RNF43 G659fs, or GFP control cDNA were overexpressed in HEK293T cells. Analysis with eVIP2 predicted that the frameshift at position 117 was a loss-of-function mutation, as expected. The second frameshift at position 659, was, surprisingly, predicted to be a gain-of-function mutation. Additional eVIP Pathways analysis of RNF43 G659fs predicted 10 pathways to be significantly altered, including TNF alpha via NFKB signaling, KRAS signaling, and hypoxia. To validate these predictions, we performed reporter assays and found that all eVIP2 impactful pathways tested in the assay were activated by expression of RNF43 G659fs, but not by expression of RNF43 WT, supporting that RNF43 G659fs is a gain-of-function mutation and its effect on the identified pathways. The eVIP2 method is an important step towards overcoming the current challenge of variant interpretation in the implementation of precision medicine. eVIP2 is available at https://github.com/BrooksLabUCSC/eVIP2.

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eVIP2: Expression-based variant impact phenotyping to predict the function of gene variants

PLoS Computational Biology ◽

10.1371/journal.pcbi.1009132 ◽

2021 ◽

Vol 17 (7) ◽

pp. e1009132

Author(s):

Alexis M. Thornton ◽

Lishan Fang ◽

April Lo ◽

Maria McSharry ◽

David Haan ◽

...

Keyword(s):

Gene Expression ◽

Colon Adenocarcinoma ◽

Mapk Signaling ◽

Negative Regulator ◽

Gene Variants ◽

Missense Mutations ◽

Loss Of Function ◽

Functional Impact ◽

Pathways Analysis ◽

The Impact

While advancements in genome sequencing have identified millions of somatic mutations in cancer, their functional impact is poorly understood. We previously developed the expression-based variant impact phenotyping (eVIP) method to use gene expression data to characterize the function of gene variants. The eVIP method uses a decision tree-based algorithm to predict the functional impact of somatic variants by comparing gene expression signatures induced by introduction of wild-type (WT) versus mutant cDNAs in cell lines. The method distinguishes between variants that are gain-of-function, loss-of-function, change-of-function, or neutral. We present eVIP2, software that allows for pathway analysis (eVIP Pathways) and usage with RNA-seq data. To demonstrate the eVIP2 software and approach, we characterized two recurrent frameshift variants in RNF43, a negative regulator of Wnt signaling, frequently mutated in colorectal, gastric, and endometrial cancer. RNF43 WT, RNF43 R117fs, RNF43 G659fs, or GFP control cDNA were overexpressed in HEK293T cells. Analysis with eVIP2 predicted that the frameshift at position 117 was a loss-of-function mutation, as expected. The second frameshift at position 659 has been previously described as a passenger mutation that maintains the RNF43 WT function as a negative regulator of Wnt. Surprisingly, eVIP2 predicted G659fs to be a change-of-function mutation. Additional eVIP Pathways analysis of RNF43 G659fs predicted 10 pathways to be significantly altered, including TNF-α via NFκB signaling, KRAS signaling, and hypoxia, highlighting the benefit of a more comprehensive approach when determining the impact of gene variant function. To validate these predictions, we performed reporter assays and found that each pathway activated by expression of RNF43 G659fs, but not expression of RNF43 WT, was identified as impacted by eVIP2, supporting that RNF43 G659fs is a change-of-function mutation and its effect on the identified pathways. Pathway activation was further validated by Western blot analysis. Lastly, we show primary colon adenocarcinoma patient samples with R117fs and G659fs variants have transcriptional profiles similar to BRAF missense mutations with activated RAS/MAPK signaling, consistent with KRAS signaling pathways being GOF in both variants. The eVIP2 method is an important step towards overcoming the current challenge of variant interpretation in the implementation of precision medicine. eVIP2 is available at https://github.com/BrooksLabUCSC/eVIP2.

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O4.7. PLACENTAL GENE EXPRESSION, OBSTETRICAL HISTORY AND POLYGENIC RISK FOR SCHIZOPHRENIA

Schizophrenia Bulletin ◽

10.1093/schbul/sby015.213 ◽

2018 ◽

Vol 44 (suppl_1) ◽

pp. S85-S86

Author(s):

Gianluca Ursini ◽

Giovanna Punzi ◽

Qiang Chen ◽

Stefano Marenco ◽

Joshua F Robinson ◽

...

Keyword(s):

Gene Expression ◽

Polygenic Risk

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O11.1. MULTIPLE GENOMIC MEASURES OF SCHIZOPHRENIA RISK ARE RELATED TO CORTICAL THICKNESS IN TYPICALLY DEVELOPING YOUTH AND YOUNG ADULTS

Schizophrenia Bulletin ◽

10.1093/schbul/sbaa028.061 ◽

2020 ◽

Vol 46 (Supplement_1) ◽

pp. S27-S27

Author(s):

Maria Jalbrzikowski ◽

Lambertus Klei ◽

William Foran ◽

Beatriz Luna ◽

Bernie Devlin

Keyword(s):

Gene Expression ◽

Brain Development ◽

Cortical Thickness ◽

Principal Components ◽

Expression Profiles ◽

Gene Expression Profiles ◽

Principal Component ◽

Typically Developing ◽

Risk Genes ◽

Polygenic Risk

Abstract Background The incidence of psychotic disorders increases in adolescence and young adulthood. Transition to a psychotic disorder is associated with atypical development of brain structures, specifically protracted developmental course. It is unknown how polygenic risk for schizophrenia and gene expression profiles of schizophrenia risk genes affect typical brain development. The goal of the current study is to examine relationships multiple genomic measures associated with schizophrenia risk and structural neuroimaging measures thickness in typically developing youth. Methods We combined structural neuroimaging and genetic data from three different cohorts of typically developing youth (N=994, 5–30 years old): the Philadelphia Neurodevelopmental Cohort, Pediatric Imaging Neurocognition and Genetics Study, and a locally collected sample at the University of Pittsburgh. All youth were free from psychiatric disorders and not taking psychiatric medications. We used Freesurfer to process the T1-weighted structural scans and calculate subcortical volumes, cortical thickness, and surface area measurements. After regressing out study, sex, ancestry eigenvectors, and grey matter signal-to-noise ratio, we ran principal components analysis on all neuroimaging measures (N=156). We calculated a schizophrenia polygenic risk score using genome-wide summary statistics from the Psychiatric Genome Consortium. Using a generalized linear model, each of the top five principal components was evaluated in relation to the risk score. We then used a computational method, Predixcan, to calculate expected gene expression profiles from the genotype data. We selected 125 genes that were associated with schizophrenia in a previous case-control comparison. Elastic net regression was used to determine significant associations between individual gene expression and the principal components. Results Schizophrenia polygenic risk was statistically associated with the 5th principal component (b=-0.10, p=0.001), which consisted of contributions from multiple measures of cortical thickness. Reduced cortical thickness in frontal and temporal regions was associated with increased genetic liability for schizophrenia. Increased cortical thickness in sensory-motor areas was associated with higher schizophrenia polygenic risk scores. This relationship remained when age was included as a predictor of interest and there were no statistically significant interactions between schizophrenia polygenic risk and age. Sixteen unique gene expression profiles were also associated with this principal component, significantly increasing the proportion of variance explained in this measure (from ~1% with the schizophrenia polygenic risk only to ~6% when including the additional gene expression measures). Many of the genes significantly associated with this principal component have important roles during early fetal brain development, including neuronal migration (e.g., SDCCAG8) and DNA repair (e.g., MLH1). Discussion These results suggest that that genetic risk for schizophrenia has a consistent influence on subtle, individual differences in a distinct spatial pattern of cortical thickness across typical development. This spatial pattern of cortical thickness is also associated with schizophrenia risk genes that have important functions during early brain development. Taken together, these findings suggest that increased genetic risk for schizophrenia is related to early subtle alterations during early brain development, setting up individuals with higher risk profiles to have a small biological vulnerability for later developing the illness.

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Novel polygenic risk score as a translational tool linking depression-related changes in the corticolimbic transcriptome with neural face processing and anhedonic symptoms

10.1101/556852 ◽

2019 ◽

Author(s):

Klara Mareckova ◽

Colin Hawco ◽

Fernanda C. Dos Santos ◽

Arin Bakht ◽

Navona Calarco ◽

...

Keyword(s):

Gene Expression ◽

Risk Score ◽

Clinical Sample ◽

Expression Patterns ◽

Neural Response ◽

Postmortem Brain ◽

Polygenic Risk Score ◽

Least Squares Regression ◽

Social Stimuli ◽

Polygenic Risk

ABSTRACTConvergent data from imaging and postmortem brain transcriptome studies implicate corticolimbic circuit (CLC) dysregulation in the pathophysiology of depression. To more directly bridge these lines of work, we generated a novel transcriptome-based polygenic risk score (T-PRS), capturing subtle shifts towards depression-like gene expression patterns in key CLC regions, and mapped this T-PRS onto brain function and related depressive symptoms in a non-clinical sample of 478 young adults (225 men; age 19.79+/−1.24) from the Duke Neurogenetics Study. First, T-PRS was generated based on common functional SNPs shifting CLC gene expression towards a depression-like state. Next, we used multivariate partial least squares regression to map T-PRS onto whole-brain activity patterns during perceptual processing of social stimuli (i.e., human faces). For validation, we conducted a comparative analysis with a PRS summarizing depression risk variants identified by the Psychiatric Genomics Consortium (PGC-PRS). Sex was modeled as moderating factor. We showed that T-PRS was associated with widespread reductions in neural response to neutral faces in women and to emotional faces and shapes in men (multivariate p<0.01). This female-specific reductions in neural response to neutral faces was also associated with PGC-PRS (multivariate p<0.03). Reduced reactivity to neutral faces was further associated with increased self-reported anhedonia. We conclude that women with functional alleles mimicking the postmortem transcriptomic CLC signature of depression have blunted neural activity to social stimuli, which may be expressed as higher anhedonia.

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